The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional
Jeremy Gwinnup, Joshua Sandvick, Michael Hutt, Grant Erdmann, John Duselis, James Davis
Abstract
AFRL-Ohio State extends its usage of visual domain-driven machine translation for use as a peer with traditional machine translation systems. As a peer, it is enveloped into a system combination of neural and statistical MT systems to present a composite translation.- Anthology ID:
- W18-6440
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 612–615
- Language:
- URL:
- https://aclanthology.org/W18-6440
- DOI:
- 10.18653/v1/W18-6440
- Cite (ACL):
- Jeremy Gwinnup, Joshua Sandvick, Michael Hutt, Grant Erdmann, John Duselis, and James Davis. 2018. The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 612–615, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- The AFRL-Ohio State WMT18 Multimodal System: Combining Visual with Traditional (Gwinnup et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/W18-6440.pdf